Data pipelines & evaluation
Support Ticket Analytics
This project extracts and organizes support-ticket data for analysis. It preserves the link between a result and its source, handles interrupted processing, and separates data preparation from the question of whether a classifier is good enough to use.
- My part
- Pipeline development and model evaluation.
- Private project
- The classifier did not meet its acceptance criteria and was not promoted.
Keeping track of the source
Extraction is read-only, with isolated local stores and resumable processing. Source reconciliation checks whether the local population matches the intended ticket set. Message-direction resolution is a separate data preparation step.
Versioned outputs and source references make a result inspectable later. These checks matter before trying to infer patterns from the tickets.
A classifier I held back
The recorded classifier evaluation reported 0.460 accuracy and 0.436 macro F1. Those results did not meet the project’s acceptance criteria, so the classifier was not promoted.
The extraction and reconciliation work was still useful. Its completion did not make the model’s predictions dependable enough to publish as conclusions.